Probabilistic programming for planning problems

Probabilistic programing is an emerging field at the intersection of statistical learning and programming languages. An appealing property of probabilistic programming languages (PPL) is their support for constructing arbitrary probability distributions. This allows one to model many different domains and solve a variety of problems. We show the link between probabilistic planning and PPLs by introducing a translation that allows one to map probabilistic planning problems onto parameter learning in PPLs. The advantage of our approach is twofold. Firstly, having the expressivity of a programming language simplifies modeling compared to using existing planning languages such as PPDDL. Secondly, there exist effective general-purpose learning algorithms that - having the correct encoding - can readily be used to learn optimal policies. In this paper we use ProbLog - a probabilistic version of Prolog - as programming language, but our approach can be applied on any other PPL as well.status: publishe